Rapid lake expansion has brought Selin Co on the Tibetan Plateau close to a low-lying overtopping threshold. This study combined HBV-based runoff simulation, lake water-balance reconstruction, ERA5-derived wind–wave analysis, and CMIP6 multi-model projections to reconstruct lake-level changes during 2013–2023, assess the possible wind–wave contribution to the 2023 overtopping event, and estimate the timing and inter-model spread of lower-bound threshold exceedance. The HBV model captured the main seasonal runoff pattern, with a daily NSE of 0.75. Reconstructed monthly lake levels agreed well with satellite altimetry observations (R² = 0.895; NSE = 0.852). Among the water-balance components considered, the reconstructed lake-level rise was more closely related to effective catchment inflow than to direct precipitation over the lake surface. For the 2023 event, calculations using representative parameters produced a maximum wave run-up of 1.22 m and a maximum wind setup of 0.075 m, suggesting that wave run-up was the main short-term wind–wave contribution when the lake level was already high. Under all four SSP scenarios, the ensemble median lake level continued to rise. The first model-indicated exceedance of the conservative lower-bound still-water threshold occurred mainly in the early 2030s, with most estimates concentrated in 2031–2034. These estimates indicate a period of increasing proximity to the threshold rather than exact dates or probabilities of future overtopping. Considerable uncertainty remains because runoff calibration was limited to one year, model parameters were transferred to ungauged tributaries, and the representations of climate forcing, lake ice, evaporation, and wind–wave processes were simplified. Overall, the results indicate that threshold proximity at Selin Co is associated with long-term lake-water accumulation, while short-term wind–wave forcing may contribute during periods of high lake level, and highlight the need for site-specific monitoring to better constrain the overtopping threshold and its potential impacts.

Future climate change is expected to modify the magnitude and timing of agricultural irrigation demand, but the mechanisms remain uncertain in humid monsoon regions where increased precipitation may offset warming-induced evapotranspiration. This study assesses future irrigation water use (IWU) over the Poyang Lake basin, China, using a previously developed NOAH-HMS land surface–hydrological model coupled with a crop-specific irrigation (CDI) scheme that distinguishes paddy rice and non-rice crops. Simulations are driven by bias-corrected CORDEX–East Asia climate projections under RCP2.6 and RCP8.5 for the mid-century (2031–2060) and late-century (2065–2094) periods. Results show that basin-mean IWU decreases in most future scenarios by about 3%–9%, mainly because projected precipitation increases largely offset warming-enhanced evapotranspiration. However, this basin-scale decline conceals seasonal and spatial heterogeneity. Regional IWU exhibits a “summer decline–autumn increase” pattern, with reductions in July–August and increases in October–November. Although rice IWU decreases, its within-season rise during the growing period becomes steeper. Non-rice IWU remains concentrated in late autumn and shows stronger sensitivity to warming and drying. Precipitation–temperature quadrant analysis shows that IWU in Dry–Hot years is generally higher than in wetter states, with the strongest increase occurring in the mid-century. Precipitation remains the primary driver of interannual IWU variability, but temperature exerts increasing influence toward the late century. These results demonstrate that basin-mean IWU changes alone could underestimate future irrigation risks, emphasizing the need to consider seasonal redistribution, crop-specific responses, local hotspots, and compound extremes in agricultural water management.

The accelerating degradation of permafrost on the Qinghai-Tibet Plateau (QTP) is a critical driver of regional and global climate change. However, conventional models often limit our understanding by overlooking thermal memory and failing to deconstruct complex spatial dynamics. This study introduces a novel diagnostic framework that pairs a thermal-memory-aware machine learning model with a multi-scale spatiotemporal analysis system to overcome these limitations. Our reconstruction from 1960 to 2020 reveals that the total permafrost area shrank by approximately 16% from its peak, while the mean active layer thickness (ALT) deepened, with degradation accelerating sharply after the 1980s. Vertically, we identify a systematic misalignment between the elevation of maximum permafrost stability (peak area) and maximum thermal sensitivity (peak ALT), the magnitude of which serves as a robust indicator of basin-scale vulnerability. Horizontally, we reveal a critical spatiotemporal mismatch: the geometric centroid of the permafrost area remains relatively stable, while its thermal center of mass exhibits large, volatile oscillations. This decoupling is driven by the contrast between rapid degradation at the warm, wet margins and the anchoring effect of the vast thermal inertia in the cold, arid core. Ultimately, our study reveals that permafrost degradation is a complex, multi-scale process rather than a uniform retreat. The diagnostic framework and the identified spatiotemporal decoupling provide a new perspective for assessing the stability and vulnerability of cryospheric systems in a warming world.

Global climate change has expanded floodplain wetland areas and accelerated water recession rates, which are expected to alter soil organic carbon (SOC) content at larger scales via moisture-driven ecohydrological processes. Microorganisms act as central regulators of SOC dynamics through their dual roles in organic matter decomposition and necromass production, both of which are governed by microbial community structure and function. However, how moisture influences these microbial traits and necromass formation to mediate SOC dynamics in floodplain wetlands remains unclear. Here, we performed a comprehensive analysis of microbial necromass carbon (C), community diversity and composition, and C metabolism-associated functions to decipher microbial-mediated SOC dynamics along a moisture gradient in Poyang Lake floodplains. We observed that SOC decreased by 62.1 % as soil moisture dropped from 53.3 % to 16.6 %, primarily attributed to the depletion of fungal necromass C rather than bacterial necromass C. Fungal community composition was the key microbial factor influencing SOC. Fungal taxonomic and functional groups were more closely associated with SOC than bacterial counterparts. Moreover, reduced moisture shifted microbial life-history strategies from high yield to resource acquisition, enhancing the degradation of recalcitrant C (e.g., lignin) rather than labile C (e.g., starch and cellulose). These shifts in microbial metabolic functions contributed to decreased microbial (particularly fungal) necromass and SOC under low moisture. Our study highlights the critical roles of fungal necromass and community composition in driving SOC dynamics under varying moisture conditions, providing novel microbial insights for assessing the responses of floodplain wetland soil C pools to hydrological changes.

Moisture transport above the Tibetan Plateau (TP) plays a crucial role in supplying water resources and maintaining the regional moisture budget, particularly in the major mountain ranges that give rise to many of Asia's great rivers. This study examines the sustainability of moisture transport and the mechanisms influencing it in major mountainous regions. The results indicate that moisture transport across the TP has remained relatively stable, with a slight increasing trend in recent decades, suggesting a sustained water supply from the “Asian Water Tower.” The increase in net moisture flux, driven by enhanced moisture inflow at the southern boundary and reduced outflow at the eastern boundary, is the primary cause of the humidification trend over the TP. A key physical mechanism maintaining moisture transport is the coupling between low-level convergence and upper-level divergence, forming a vertical chain of “convergence–condensation–precipitation–divergence.” However, due to differences in topography and moisture content, the altitude and intensity of moisture convergence vary spatially. Over the southern mountains, convergence is stronger and occurs at lower altitudes, leading to a much lower maximum precipitation height compared to the northern mountains, where convergence is weaker and occurs at higher altitudes. Moreover, the convergence layer is shallower in the south than in the north, reflecting spatial differences in the vertical distance over which moisture is converted into precipitation. Despite the relatively stable moisture supply, rapid warming over the southern TP has increased the atmospheric water-holding capacity. However, the significant decrease in atmospheric specific humidity makes it more difficult for the atmosphere to reach saturation, leading to a decline in precipitation in the southern region. Plain language summary Moisture transport is a critical component of the global hydrological cycle and a key factor influencing precipitation generation and variability. This study investigates the spatiotemporal changes in moisture transport over the Tibetan Plateau (TP) to assess the sustainability of moisture supply in this region. From both thermodynamic and dynamic perspectives, we explore the physical mechanisms governing moisture transport. The results indicate that moisture transport over the TP and its mountainous subregions has remained relatively stable, with a slight increasing trend in recent decades. The increase in net moisture flux is identified as the primary driver of increased precipitation over the TP. Influenced by complex topography of the TP, a coupling relationship between low-level moisture convergence and upper-level divergence has developed. This coupling facilitates the continuous transport of moisture onto the plateau and helps maintain the atmospheric moisture supply over the TP. The study further reveals that over the southern mountains, moisture convergence occurs at lower altitudes and with greater intensity, whereas over the northern mountains it occurs at higher altitudes with weaker intensity. This results in a pattern in which the maximum precipitation height over the southern region is much lower than that over the northern mountainous areas. Despite the stable to slightly increasing trend in moisture transport, atmospheric specific humidity has decreased over the southern TP, in contrast to increasing specific humidity in the northwest. Together with rising temperatures that make atmospheric saturation more difficult to achieve in the south, these changes help explain the recent decline in precipitation over the southeastern TP and the concurrent increase in precipitation over the northwestern TP.

Sediments serve as a major reservoir for endogenous pollutants, but dynamic changes in lake ecosystems could substantially modify sediment properties, and their implications on sediment arsenic (As) behaviors remain insufficiently understood. This study combined field investigations in a natural lake and laboratory experiments to examine As dynamics at the sediment water interface (SWI), using high-resolution dialysis techniques coupled with biochemical analyses. Our results showed that sediment bulking could enhance As release at the SWI. The degradation of organic matter by microbes reduces sediment shear strength, increases porosity through bulking, and alongside the dissolution of iron/manganese minerals, expands the mobility of dissolved As in pore water. Laboratory experiment further confirmed that sediments with high organic matter exhibit reduced shear strength, promoting sediment bulking and pore development, which in turn result in a 93.95% increase in As diffusion flux and a 25.58% increase in release flux. This study highlights the pivotal role of sediment physical deformation in regulating endogenous As dynamics, advances our understanding of As migration and transformation pathways within sediments, and provides a scientific basis for pollution mitigation strategies.

The current understanding of inorganic solute transport, hydrochemical mechanisms, and water quality status in the surface waters of the Yellow River headwaters (HWYR) remains limited. This study investigated the spatial variation, formation mechanisms, and hydrochemical ion sources while assessing surface water quality. The results indicate that HWYR surface waters are weakly alkaline (pH: 8.30–8.51) and fresh (total dissolved solids (TDS): 308–397 mg/L; mineralization: 296–378 mg/L; total hardness (TH): 200–233 mg/L). The Ngoring Lake exhibited significant spatial heterogeneity in hydrochemical parameters. The confluence of the Gyaring Lake and Duoqu River showed peak Ca2+, SO42−, total phosphorus, and TH, while the mainstem Yellow River had the highest TDS and mineralization. Riverine total nitrogen and NO3− exceeded lake concentrations, indicating nitrogen cycling significantly drives hydrochemical processes. HCO3− dominated anions (>40

Study region This study focuses on the Tuotuo River Basin (TRB), located in the headwater region of the Yangtze River. Study focus We proposed an Empirical Orthogonal Function (EOF)-based framework to aggregate spatially distributed precipitation data from the China Meteorological Forcing Dataset (CMFD) into a spatially weighted series for the HBV model, aiming to improve the simulation of seasonal flood peaks and hydrological processes in the TRB. New hydrological insights for the region Basin-scale precipitation is controlled mainly by a coherent large-scale pattern with strong topographic modulation. The EOF based weighted precipitation better reflects basin-scale precipitation concentration, while its slightly higher monthly medians and larger spring dispersion suggest enhanced sensitivity to spatial heterogeneity during the pre-monsoon period. The EOF-based framework significantly enhanced the simulation of seasonal flood peaks in the TRB, particularly in terms of timing and magnitude of high-flow events from May to October.

The Yarlung Tsangpo River (YTR), a major plateau river on the southern Tibetan Plateau (TP), exhibits complex hydrological processes under ongoing climate change. Although freeze-thaw are recognized as critical factors influencing plateau river runoff, the underlying generation mechanisms and the quantitative contributions of various water sources remain insufficiently understood. By integrating high-frequency monitoring and isotopic tracing (2020-2023) with End-Member Mixing Analysis, this study quantified the spatial and seasonal contributions of glacial meltwater, snow meltwater, precipitation and groundwater to streamflow, in the middle YTR across four freeze-thaw stages from April to July. An "Inverse Elevation Effect" on delta 18O was observed along the Yarla Shampo Glacier hillslope, arising from strong transverse vapor mixing during atmospheric moisture transport. Glacial meltwater dominated streamflow, especially in the upper watershed during the initial melting stage (67.2%), but contributions shifted seasonally: groundwater and snow meltwater increased substantially during accelerated and rapid melting stages, while precipitation became dominant (36.6-37.5%) in the final melting stage at July, indicating a major seasonal transition in hydrological drivers. Notably, whereas surface water sources fluctuated widely, groundwater maintained a remarkably consistent seasonal flux, emphasizing its role as a fundamental hydrological buffer that supports basin-wide water resilience through the freeze-thaw cycle. Spatially, contributions from groundwater and snow meltwater were consistently higher in downstream regions than upstream, reflecting the cumulative input from tributaries and subsurface flow along the watershed. Integrating these processes, this study proposed a novel conceptual model of freeze-thaw-driven runoff generation that incorporated topography and vegetation interactions to elucidate spatiotemporal patterns in streamflow composition. This study provides quantitative insights for refining runoff generation models and predicting hydrological responses of high-altitude cold regions to climate warming, offering guidance for groundwater management and climate adaptation strategies.

The correction of gridded precipitation products (GPPs) is a critical approach for obtaining high-quality precipitation data in data-scarce regions. However, a comprehensive understanding of the accuracy of corrected reanalysis and satellite precipitation products remains lacking in such regions, posing a challenge for selecting a higher-quality corrected GPP. In this study, two representative and newly released GPPs, including the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG) V07_FR (GPMv07) and the European Centre for Medium-Range Weather Forecasts Reanalysis V5 for Land (ERA5-Land), were evaluated and corrected using three machine learning methods in the Tianshan Mountains in China. The potential advantages of the optimally corrected GPP were further identified. The results showed that both products exhibited significant errors, as reflected by the fact that the proportion of stations with absolute percent bias (PB) exceeding 20% was above 64.0%. In addition, the accuracy of GPMv07 and ERA5-Land exhibited altitude dependence. The method using random forest for correcting GPMv07 (RF-G) outperformed other bias-correction methods in reproducing monthly precipitation. Compared with the original GPMv07, the RF-G method improved the mean Kling–Gupta efficiency (KGE) and the proportion of stations with absolute PB no greater than 20% by 0.57 and 67.7%, respectively. Furthermore, the monthly precipitation data corrected by the RF-G method was superior to several state-of-the-art merged precipitation products, including CMFD, AIMERG, AERA5-Asia, and GMCP, at the monthly scale. These findings highlight the value of correcting the newly released GPMv07 and can provide support for water resources management in data-scarce mountainous areas.

The Tibetan Plateau, often called the "Asian water tower," is highly sensitive to extreme droughts and intense precipitation because of its complex topography and strong monsoonal influences, which pose significant risks to regional and downstream water and ecosystem security. This study uses China Meteorological Forcing Dataset (CMFD) daily precipitation data for 1979-2015 and bias-corrected and downscaled Coupled Model Intercomparison Project phase 6 (CMIP6) daily precipitation data for 2016-2100 to identify and project two types of sequential compound events: drought followed by extreme precipitation (CDEP) and extreme precipitation followed by drought (CWDE). A dynamic identifica-tion framework was applied, defining drought with a 30-day precipitation threshold, defining extreme precipitation with a 3-day threshold, and pairing events within intervals from 1 to 3 months. Our results show that the frequency of both CDEP and CWDE increases markedly with longer lag times. During the historical period, event hotspots were concentrated in the southern and central Plateau. Under future shared socioeconomic pathway (SSP) scenarios, CDEP risk expands northward into interior arid regions such as northern Tibet and the Qaidam Basin, while CWDE remains centered in the south and central Plateau but extends toward inland areas. Importantly, the increase in event frequency is not monotonic with warming; the strongest enhancement occurs under moderate emissions, specifically SSP2-4.5 and SSP3-7.0. Overall, warming and a moister atmosphere intensify the alternation between dry and wet extremes, driven by enhanced monsoonal moisture transport, orographic lifting, and stronger land-atmosphere coupling. These findings provide a scientific basis for improving drought and flood risk management and climate adaptation strategies in high mountain watersheds of the Tibetan Plateau.

Endorheic lakes on the Qinghai-Tibet Plateau (QTP) serve as sensitive indicators of climate change, with their water volume fluctuations directly reflecting the response characteristics of regional hydrological processes. However, the dynamics of lake water sources under climate change remain unclear. We estimated the water storage of 288 endorheic lakes, identified 288 dominant recharge types based on basin water balance analysis, and examined the spatiotemporal dynamics of lake water source structures over the past two decades. Our findings indicate that endorheic lakes on the QTP have exhibited a pronounced expansion trend in recent years, with distinct spatial variations in their dominant structural characteristics: rainfall-dominated and snowmeltdominated lakes show a clear north-south boundary, while groundwater-dominated lakes are primarily distributed in the northern and northwestern parts of the Qiangtang Basin. Overall, rainfall serves as the primary water source, followed by snowmelt and groundwater, with glacial meltwater contributing relatively less. Furthermore, we found that the dominant factors of lake water sources are not static: snowmelt-dominated lakes are shifting to a rainfall-dominated type, with the transitional boundary moving northward from 32.5 degrees N to 35 degrees N. Concurrently, the number of groundwater-dominated lakes first increased then decreased, exhibiting spatial patterns of initial clustering followed by dispersion because permafrost degradation is reshaping groundwaterlake connectivity. This study provides new quantitative evidence for understanding the hydrological response mechanisms of endorheic lakes on the QTP to climate change, and offers scientific support for regional water resource sustainability management and climate impact assessments.

The Tibetan Plateau (TP) is one of the most climate-sensitive regions on Earth, where soil respiration (Rs) and ecosystem respiration (Re) are expected to respond strongly to climate change. However, due to sparse observations and complex ecological processes, the spatial characteristics of respiration fluxes across the Plateau and their responses to future climate changes remain poorly understood. In this study, we developed a knowledge-guided multi-task deep neural network (KG-MTDNN) model that leverages prior ecological information and shared representations to improve the ecological plausibility and model performance of respiration simulations under data-sparse conditions. Based on this model, we conducted a comprehensive assessment of both the current status and future trajectories of Rs and Re across the TP. Our results reveal a southeast-to-northwest decreasing spatial pattern of respiration, along with a persistent increasing trend under future climate scenarios. Seasonally, the majority of carbon emissions occur during the growing season, whereas spatially, non-permafrost areas contribute most of the total emissions. In terms of Re components, Rs accounts for the dominant portion (similar to 70%) of Re, and this dominance is expected to further strengthen under ongoing climate change. Notably, according to our estimates, climate-induced enhancement of Re under high radiative forcing scenario will offset nearly 70% of the TP's current carbon sink. Our study highlights the potential risk of significantly increased carbon emissions from the Earth's Third Pole under future climate change, and improves understanding of climate-carbon cycle feedback mechanisms in cold-region ecosystems.

The Tibetan Plateau, known as the "Asian water tower", plays a critical role in regional and global hydroclimate. However, a comprehensive understanding of the spatial and topographic variations in extreme precipitation and their underlying mechanisms over the Tibetan Plateau remains limited. Leveraging two state-of-the-art daily gridded precipitation datasets-the ECMWF ERA5 reanalysis and the High-accuracy Precipitation for the Third Pole (TPHiPr), we found that extreme precipitation (identified using the metric Rx1day (the maximum 1-day precipitation amount) and Rx5day (the maximum 5-day precipitation amount)) substantially increased during 1982-2020, with the trends becoming particularly pronounced in the last two decades (2001-2020). Spatially, changes exhibited a distinct dipole pattern, characterized by northward intensification contrasting with southward weakening. Topography strongly modulated these spatial patterns. Increases in extreme and mean precipitation were amplified at higher elevations and on gentler slopes, with changes in evaporation contributing more than those in atmospheric moisture convergence. Furthermore, precipitable water exerted a stronger influence than air temperature on both interannual variability and spatial heterogeneity of extreme precipitation. These findings deepen our mechanistic understanding of climate-induced changes in the hydrological cycle over the Tibetan Plateau.

Irrigation has a notable impact on the natural environment by changing the water and energy balance at the land surface and thereby altering atmospheric processes. Assessing these impacts and estimating irrigation water demand often involves using process-based models that incorporate the representation of irrigation practices. However, current irrigation schemes are primarily tailored to arid and semi-arid regions, and there is a research gap for humid multi-cropping rice regions. In response, this study introduces a Crop-specific Dynamic Irrigation (CDI) scheme, seamlessly integrated into the land surface-hydrologic model NOAH-HMS. This development enables the differentiation of irrigation practices for rice and non-rice crops, facilitating more accurate estimates of water demand for irrigation. The newly developed model is applied to an important cropping region in southern China, the Poyang Lake Basin (PLB), where the rice cultivation area accounts for over 60% of all crop cultivation. Compared to the widely used traditional Dynamic Irrigation (DI) scheme, integrating CDI into NOAH-HMS improves the model performance in simulating irrigation water amount over the PLB, with a mean relative error between 2007-2015 reduced by 39%, and a correlation coefficient increased by +0.26. The identified impacts on the surface water and energy balance are more pronounced at local scale, especially over the intensively irrigated areas. The performed interannual variability analysis demonstrates that our irrigation scheme CDI developed in this study allows to estimate irrigation water use under different drought conditions and has the applicability of mitigating risks of crop failures due to for example compound dry and hot. We conclude that our Crop-specific Dynamic Irrigation scheme is highly advantageous for multi-cropping rice regions and holds the potential for expansion into the fully coupled atmospheric-hydrologic systems with a more comprehensive representation of human activities.

The Qinghai-Tibet Plateau (QTP), which hosts the world's largest area of alpine permafrost, is experiencing accelerated degradation due to climate warming, posing significant threats to regional hydrological cycles and ecosystem stability. While existing research has primarily focused on direct temperature impacts, the influence of precipitation and the multi-year lagged responses of permafrost thermal regimes remain insufficiently quantified. To address these gaps, we employed a machine learning approach that integrates multi-year (0-5 years) lagged climatic features (temperature and precipitation) to model permafrost distribution and active layer thickness (ALT) across the QTP from 1960 to 2020. Our comparative analysis of three machine learning paradigms revealed that CatBoost delivered superior predictive performance (testing set F1-score = 0.979; R2 = 0.791). Crucially, this high performance is directly attributable to the model's capacity to leverage a multi-year "climate memory", which highlights the importance of incorporating lagged climate features in permafrost change simulation. Interpretability analyses of the CatBoost model further reveal that winter snowfall acts as a key insulator, whereas spring and summer rainfall accelerate thawing by increasing soil thermal conductivity. Spatiotemporal analysis identified a net permafrost retreat of 2.51 x 104 km2 per decade. Notably, ALT dynamics exhibited a pronounced regime shift around 1980, transitioning from a thinning trend (-5.2 cm/decade, 1960-1980) to rapid thickening (+4.1 cm/decade, 1980-2000). These results establish that a robust understanding of QTP permafrost dynamics requires moving beyond simple temperature-driven models to incorporate the interacting roles of seasonal precipitation and cumulative climatic legacies.

Study region: China. Study focus: This study integrates the Mann-Kendall (M-K) test, Rescaled Range (R/S) analysis, and V-statistic methods to investigate the spatiotemporal patterns, persistence, and cycle lengths of extreme precipitation in China (1959–2015), offering new insights into long-term dynamics and regional variability. New hydrological insights for the region: Our analysis reveals that while total annual precipitation (TAP) has generally increased, extreme TAP shows a slight decline, highlighting its greater sensitivity to climate change. The M-K test identifies earlier abrupt changes in extreme precipitation (1964) compared to total precipitation (1981). Hurst exponent results indicate strong persistence across regions, except in southern China, where non-stationarity suggests potential trend reversals. The V-statistic identifies an 8-year cycle in southern China – the longest among all regions – pointing to sustained impacts on future variability. These findings deepen understanding of extreme precipitation behaviors, emphasizing the need for region-specific adaptation strategies under changing climate conditions.

The Weihe River Basin, a typical watershed in the Loess Plateau, is used as the research object to examine the regulation of runoff by vegetation. Here, we employed two deep learning algorithms, FNN and LSTM, for runoff modelling and adopted two modelling schemes - one incorporating NDVI and the other excluding NDVI - to examine the role of vegetation in runoff simulation. Finally, the optimal algorithm and the most appropriate modelling scheme were applied to simulate runoff variations under different scenarios. The results showed that the overall trend of NDVI in the Weihe River Basin is increasing, and the overall trend of runoff is decreasing. Incorporating NDVI into the models will significantly improve the simulation accuracy. We also found that intra-annual vegetation cover variations impact runoff processes, with wet and normal year runoff highly sensitive to these changes. Our study highlights the role of vegetation in regulating runoff.

Aquaculture promotes the accumulation of substances in sediments and alters the microenvironment state of sediment-water interfaces (SWI). However, the response mechanism of arsenic (As) dynamics in sediments to these changes remains unclear. In this study, we employed high-resolution techniques to investigate the seasonal dynamics of As at the SWI in aquaculture-impacted lake zones, systematically analyzing its mobilization pathways and associated driving mechanisms. Our findings revealed that seasonal variability significantly influenced both dissolved As concentrations in sediment porewater (ranging from 3.54 to 88.14 μg/L) and As diffusive fluxes (ranging from 1.22 to 95.58 μg·m⁻²·d⁻¹). Dissolved As were consistently higher in the aquaculture zones than non-aquaculture areas during both spring and summer. Sedimentary As fractionations differed markedly between aquaculture and non-aquaculture zones, with aquaculture sediments exhibiting higher proportions of both non-specifically adsorbed As (F1) and specifically adsorbed As (F2). Correlation analysis indicated that F1 and poorly crystalline oxyhydroxide-bound As (F3) were major contributors to As mobilization into porewater in aquaculture zones. Partial least squares path modeling revealed distinctive key mechanisms: in non-aquaculture zones, Fe/Mn (oxyhydr) oxides dominated As dissolution processes, whereas in aquaculture zones, elevated nutrient levels modified dissolved organic matter (DOM) composition, thereby altering As speciation and enhancing its dissolution. Collectively, our study advances the understanding of As biogeochemistry at the SWI in aquaculture environments and underscores the potential amplification of As mobilization due to seasonal variability, highlighting the need for continuous monitoring and management.

Global warming has been intensifying the water cycle, thereby altering regional climate systems and hydrological processes. This is particularly the case for the Poyang Lake Basin (PLB) in monsoon-controlled southeast China, where climate changes and human activities are evident. Our study aims to quantify the contributions of climate change and human activities to the spatiotemporal variations of the relevant variables across meteorological and hydrological compartments on the basin scale. This study applies the moving t-test, Mann-Kendall test, and linear regression models to quantify the impacts of climate change and human activities on changes in streamflow and lake level from 1960 to 2019. Results show that precipitation, streamflow, and air temperature have increased, but Poyang Lake level has declined. Change points in streamflow trends are identified in 1991 and 2002 and in lake level in 2003. Contribution analysis indicates that climate change is the primary driver of increased streamflow. However, after 2002, the contribution of climate change declined, while that of human activities increased. The abrupt decline in lake level is mainly attributed to anthropogenic interventions. These findings identify the dominant factors of hydrological change and provide guidance for ensuring water security and sustainable water resource management in the basin.